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We present GraPPa, an effective pre-training approach for table semanticparsing that learns a compositional inductive bias in the joint representationsof textual and tabular data. We construct synthetic question-SQL pairs overhigh-quality tables via a synchronous context-free grammar (SCFG) induced fromexisting text-to-SQL datasets. We pre-train our model on the synthetic datausing a novel text-schema linking objective that predicts the syntactic role ofa table field in the SQL for each question-SQL pair. To maintain the model'sability to represent real-world data, we also include masked language modeling(MLM) over several existing table-and-language datasets to regularize thepre-training process. On four popular fully supervised and weakly supervisedtable semantic parsing benchmarks, GraPPa significantly outperformsRoBERTa-large as the feature representation layers and establishes newstate-of-the-art results on all of them.

GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing